Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations3190
Missing cells17868
Missing cells (%)21.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory896.6 B

Variable types

Numeric12
Text2
Categorical12

Alerts

cluster_k_4 has constant value "0" Constant
CUIT is highly overall correlated with Estado and 10 other fieldsHigh correlation
Estado is highly overall correlated with CUIT and 1 other fieldsHigh correlation
TipoSocietario is highly overall correlated with CUIT and 1 other fieldsHigh correlation
anio_preinscripcion is highly overall correlated with CUIT and 2 other fieldsHigh correlation
antiguedad is highly overall correlated with anio_preinscripcion and 1 other fieldsHigh correlation
cant_Apoderado is highly overall correlated with cant_MontoLimite and 3 other fieldsHigh correlation
cant_MontoLimite is highly overall correlated with CUIT and 6 other fieldsHigh correlation
cant_antecedentes is highly overall correlated with cant_MontoLimite and 1 other fieldsHigh correlation
cant_apercibimientos is highly overall correlated with CUIT and 6 other fieldsHigh correlation
cant_autenticado is highly overall correlated with cant_MontoLimite and 1 other fieldsHigh correlation
cant_noAutenticado is highly overall correlated with cant_Apoderado and 5 other fieldsHigh correlation
cant_procesos_adjudicado is highly overall correlated with monto_total_adjudicadoHigh correlation
cant_representante is highly overall correlated with CUIT and 3 other fieldsHigh correlation
cant_sinMontoLimite is highly overall correlated with cant_Apoderado and 2 other fieldsHigh correlation
cant_socios is highly overall correlated with cant_apercibimientosHigh correlation
cant_suspensiones is highly overall correlated with CUIT and 5 other fieldsHigh correlation
dcant_procesos_adjudicado is highly overall correlated with CUITHigh correlation
dmonto_total_adjudicado is highly overall correlated with CUITHigh correlation
dtotal_articulos_provee is highly overall correlated with CUIT and 1 other fieldsHigh correlation
monto_total_adjudicado is highly overall correlated with cant_MontoLimite and 1 other fieldsHigh correlation
periodo_preinscripcion is highly overall correlated with Estado and 8 other fieldsHigh correlation
provincia is highly overall correlated with CUITHigh correlation
total_articulos_provee is highly overall correlated with cant_apercibimientosHigh correlation
Estado is highly imbalanced (68.9%) Imbalance
TipoSocietario is highly imbalanced (54.6%) Imbalance
cant_apercibimientos is highly imbalanced (87.8%) Imbalance
cant_representante is highly imbalanced (74.1%) Imbalance
cant_MontoLimite is highly imbalanced (55.0%) Imbalance
cant_socios has 61 (1.9%) missing values Missing
cant_apercibimientos has 3130 (98.1%) missing values Missing
cant_suspensiones has 3146 (98.6%) missing values Missing
cant_antecedentes has 3099 (97.1%) missing values Missing
cant_Apoderado has 1496 (46.9%) missing values Missing
cant_representante has 1055 (33.1%) missing values Missing
cant_noAutenticado has 2714 (85.1%) missing values Missing
cant_sinMontoLimite has 56 (1.8%) missing values Missing
cant_MontoLimite has 3101 (97.2%) missing values Missing
periodo_preinscripcion is highly skewed (γ1 = -56.31378191) Skewed
monto_total_adjudicado is highly skewed (γ1 = 23.82210639) Skewed
CUIT has unique values Unique
antiguedad has 295 (9.2%) zeros Zeros

Reproduction

Analysis started2025-06-18 13:00:48.067945
Analysis finished2025-06-18 13:01:58.833381
Duration1 minute and 10.77 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CUIT
Real number (ℝ)

High correlation  Unique 

Distinct3190
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.088871 × 1010
Minimum2.0049654 × 1010
Maximum3.4638952 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.8 KiB
2025-06-18T10:01:58.916854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.0049654 × 1010
5-th percentile3.0559347 × 1010
Q13.070466 × 1010
median3.0710828 × 1010
Q33.0715317 × 1010
95-th percentile3.3709919 × 1010
Maximum3.4638952 × 1010
Range1.4589298 × 1010
Interquartile range (IQR)10657560

Descriptive statistics

Standard deviation1.2417237 × 109
Coefficient of variation (CV)0.040199921
Kurtosis35.764699
Mean3.088871 × 1010
Median Absolute Deviation (MAD)4504766.5
Skewness-3.1955525
Sum9.8534984 × 1013
Variance1.5418777 × 1018
MonotonicityNot monotonic
2025-06-18T10:01:59.011848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.07164411 × 10101
 
< 0.1%
3.056921168 × 10101
 
< 0.1%
3.071150036 × 10101
 
< 0.1%
3.070841549 × 10101
 
< 0.1%
3.371228609 × 10101
 
< 0.1%
3.05831843 × 10101
 
< 0.1%
3.070853832 × 10101
 
< 0.1%
3.052141731 × 10101
 
< 0.1%
3.070851685 × 10101
 
< 0.1%
3.071021 × 10101
 
< 0.1%
Other values (3180) 3180
99.7%
ValueCountFrequency (%)
2.004965406 × 10101
< 0.1%
2.007823978 × 10101
< 0.1%
2.012819977 × 10101
< 0.1%
2.013072563 × 10101
< 0.1%
2.013458966 × 10101
< 0.1%
2.014569543 × 10101
< 0.1%
2.017367009 × 10101
< 0.1%
2.017944672 × 10101
< 0.1%
2.01885196 × 10101
< 0.1%
2.020743711 × 10101
< 0.1%
ValueCountFrequency (%)
3.463895204 × 10101
< 0.1%
3.454667147 × 10101
< 0.1%
3.450004534 × 10101
< 0.1%
3.399924211 × 10101
< 0.1%
3.371757977 × 10101
< 0.1%
3.371749961 × 10101
< 0.1%
3.371748342 × 10101
< 0.1%
3.371736076 × 10101
< 0.1%
3.371734327 × 10101
< 0.1%
3.371724817 × 10101
< 0.1%

Nombre
Text

Distinct3186
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size284.4 KiB
2025-06-18T10:01:59.168087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length135
Median length91
Mean length22.462382
Min length3

Characters and Unicode

Total characters71655
Distinct characters94
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3185 ?
Unique (%)99.8%

Sample

1st rowElectricidad Chiclana de R. Santoianni y O.S. Rodriguez
2nd rowLICICOM S.R.L.
3rd rowYLUM S.A.
4th rowCOMPAÑÍA DE HIGIENE
5th rowMATAFUEGOS ORLANDO S.R.L.
ValueCountFrequency (%)
srl 1438
 
13.1%
s.r.l 800
 
7.3%
de 437
 
4.0%
y 256
 
2.3%
cooperativa 165
 
1.5%
trabajo 139
 
1.3%
s.a 117
 
1.1%
argentina 112
 
1.0%
servicios 99
 
0.9%
la 98
 
0.9%
Other values (4203) 7306
66.6%
2025-06-18T10:01:59.418692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7778
 
10.9%
S 5180
 
7.2%
R 4963
 
6.9%
A 4747
 
6.6%
L 3964
 
5.5%
E 3506
 
4.9%
I 3384
 
4.7%
O 3030
 
4.2%
. 2974
 
4.2%
C 2305
 
3.2%
Other values (84) 29824
41.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 71655
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7778
 
10.9%
S 5180
 
7.2%
R 4963
 
6.9%
A 4747
 
6.6%
L 3964
 
5.5%
E 3506
 
4.9%
I 3384
 
4.7%
O 3030
 
4.2%
. 2974
 
4.2%
C 2305
 
3.2%
Other values (84) 29824
41.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 71655
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7778
 
10.9%
S 5180
 
7.2%
R 4963
 
6.9%
A 4747
 
6.6%
L 3964
 
5.5%
E 3506
 
4.9%
I 3384
 
4.7%
O 3030
 
4.2%
. 2974
 
4.2%
C 2305
 
3.2%
Other values (84) 29824
41.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 71655
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7778
 
10.9%
S 5180
 
7.2%
R 4963
 
6.9%
A 4747
 
6.6%
L 3964
 
5.5%
E 3506
 
4.9%
I 3384
 
4.7%
O 3030
 
4.2%
. 2974
 
4.2%
C 2305
 
3.2%
Other values (84) 29824
41.6%
Distinct1171
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Memory size233.6 KiB
2025-06-18T10:01:59.584110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9996865
Min length9

Characters and Unicode

Total characters31899
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique530 ?
Unique (%)16.6%

Sample

1st row18/08/2016
2nd row15/09/2016
3rd row24/08/2016
4th row26/10/2016
5th row02/11/2016
ValueCountFrequency (%)
03/11/2016 19
 
0.6%
01/11/2016 19
 
0.6%
14/11/2016 18
 
0.6%
07/11/2016 17
 
0.5%
10/01/2017 14
 
0.4%
18/04/2017 14
 
0.4%
17/11/2021 13
 
0.4%
08/11/2016 13
 
0.4%
26/10/2016 13
 
0.4%
15/11/2016 13
 
0.4%
Other values (1162) 3038
95.2%
2025-06-18T10:01:59.817870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7191
22.5%
/ 6378
20.0%
1 5863
18.4%
2 5667
17.8%
7 1860
 
5.8%
6 1151
 
3.6%
8 1067
 
3.3%
9 866
 
2.7%
3 729
 
2.3%
4 569
 
1.8%
Other values (9) 558
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31899
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7191
22.5%
/ 6378
20.0%
1 5863
18.4%
2 5667
17.8%
7 1860
 
5.8%
6 1151
 
3.6%
8 1067
 
3.3%
9 866
 
2.7%
3 729
 
2.3%
4 569
 
1.8%
Other values (9) 558
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31899
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7191
22.5%
/ 6378
20.0%
1 5863
18.4%
2 5667
17.8%
7 1860
 
5.8%
6 1151
 
3.6%
8 1067
 
3.3%
9 866
 
2.7%
3 729
 
2.3%
4 569
 
1.8%
Other values (9) 558
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31899
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7191
22.5%
/ 6378
20.0%
1 5863
18.4%
2 5667
17.8%
7 1860
 
5.8%
6 1151
 
3.6%
8 1067
 
3.3%
9 866
 
2.7%
3 729
 
2.3%
4 569
 
1.8%
Other values (9) 558
 
1.7%

Estado
Categorical

High correlation  Imbalance 

Distinct8
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Memory size242.0 KiB
Inscripto
2689 
Desactualizado Por Documentos Vencidos
 
264
Desactualizado Por Mantencion Formulario
 
95
Pre Inscripto
 
70
Desactualizado Por Clase
 
40
Other values (3)
 
31

Length

Max length40
Median length9
Mean length12.676388
Min length9

Characters and Unicode

Total characters40425
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDesactualizado Por Documentos Vencidos
2nd rowInscripto
3rd rowInscripto
4th rowInscripto
5th rowDesactualizado Por Documentos Vencidos

Common Values

ValueCountFrequency (%)
Inscripto 2689
84.3%
Desactualizado Por Documentos Vencidos 264
 
8.3%
Desactualizado Por Mantencion Formulario 95
 
3.0%
Pre Inscripto 70
 
2.2%
Desactualizado Por Clase 40
 
1.3%
Con Solicitud De Baja 16
 
0.5%
En Evaluacion 12
 
0.4%
Suspendido 3
 
0.1%
(Missing) 1
 
< 0.1%

Length

2025-06-18T10:01:59.896060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:01:59.966678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
inscripto 2759
61.6%
desactualizado 399
 
8.9%
por 399
 
8.9%
documentos 264
 
5.9%
vencidos 264
 
5.9%
mantencion 95
 
2.1%
formulario 95
 
2.1%
pre 70
 
1.6%
clase 40
 
0.9%
con 16
 
0.4%
Other values (6) 75
 
1.7%

Most occurring characters

ValueCountFrequency (%)
o 4681
11.6%
c 3809
9.4%
s 3729
9.2%
i 3659
9.1%
n 3615
8.9%
t 3533
8.7%
r 3418
8.5%
p 2762
 
6.8%
I 2759
 
6.8%
a 1483
 
3.7%
Other values (18) 6977
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40425
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 4681
11.6%
c 3809
9.4%
s 3729
9.2%
i 3659
9.1%
n 3615
8.9%
t 3533
8.7%
r 3418
8.5%
p 2762
 
6.8%
I 2759
 
6.8%
a 1483
 
3.7%
Other values (18) 6977
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40425
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 4681
11.6%
c 3809
9.4%
s 3729
9.2%
i 3659
9.1%
n 3615
8.9%
t 3533
8.7%
r 3418
8.5%
p 2762
 
6.8%
I 2759
 
6.8%
a 1483
 
3.7%
Other values (18) 6977
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40425
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 4681
11.6%
c 3809
9.4%
s 3729
9.2%
i 3659
9.1%
n 3615
8.9%
t 3533
8.7%
r 3418
8.5%
p 2762
 
6.8%
I 2759
 
6.8%
a 1483
 
3.7%
Other values (18) 6977
17.3%

TipoSocietario
Categorical

High correlation  Imbalance 

Distinct9
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Memory size302.5 KiB
Sociedad Responsabilidad Limitada
2342 
Otras Formas Societarias
306 
Sociedad Anónima
 
188
Cooperativas
 
178
Sociedades De Hecho
 
105
Other values (4)
 
70

Length

Max length33
Median length33
Mean length29.165256
Min length12

Characters and Unicode

Total characters93008
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSociedades De Hecho
2nd rowSociedad Responsabilidad Limitada
3rd rowSociedad Anónima
4th rowSociedad Responsabilidad Limitada
5th rowSociedad Responsabilidad Limitada

Common Values

ValueCountFrequency (%)
Sociedad Responsabilidad Limitada 2342
73.4%
Otras Formas Societarias 306
 
9.6%
Sociedad Anónima 188
 
5.9%
Cooperativas 178
 
5.6%
Sociedades De Hecho 105
 
3.3%
Organismo Publico 35
 
1.1%
Persona Física 25
 
0.8%
Unión Transitoria de Empresas 9
 
0.3%
Talleres protegidos de Producción 1
 
< 0.1%
(Missing) 1
 
< 0.1%

Length

2025-06-18T10:02:00.045909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:02:00.108397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sociedad 2530
28.2%
responsabilidad 2342
26.1%
limitada 2342
26.1%
otras 306
 
3.4%
formas 306
 
3.4%
societarias 306
 
3.4%
anónima 188
 
2.1%
cooperativas 178
 
2.0%
de 115
 
1.3%
sociedades 105
 
1.2%
Other values (11) 255
 
2.8%

Most occurring characters

ValueCountFrequency (%)
a 13884
14.9%
i 13105
14.1%
d 12308
13.2%
o 6157
 
6.6%
s 5999
 
6.4%
e 5823
 
6.3%
5784
 
6.2%
t 3142
 
3.4%
c 3108
 
3.3%
S 2941
 
3.2%
Other values (24) 20757
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 13884
14.9%
i 13105
14.1%
d 12308
13.2%
o 6157
 
6.6%
s 5999
 
6.4%
e 5823
 
6.3%
5784
 
6.2%
t 3142
 
3.4%
c 3108
 
3.3%
S 2941
 
3.2%
Other values (24) 20757
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 13884
14.9%
i 13105
14.1%
d 12308
13.2%
o 6157
 
6.6%
s 5999
 
6.4%
e 5823
 
6.3%
5784
 
6.2%
t 3142
 
3.4%
c 3108
 
3.3%
S 2941
 
3.2%
Other values (24) 20757
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 13884
14.9%
i 13105
14.1%
d 12308
13.2%
o 6157
 
6.6%
s 5999
 
6.4%
e 5823
 
6.3%
5784
 
6.2%
t 3142
 
3.4%
c 3108
 
3.3%
S 2941
 
3.2%
Other values (24) 20757
22.3%

periodo_preinscripcion
Real number (ℝ)

High correlation  Skewed 

Distinct79
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201722.27
Minimum0
Maximum202212
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size49.8 KiB
2025-06-18T10:02:00.217754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile201610
Q1201702
median201708.5
Q3201901
95-th percentile202109
Maximum202212
Range202212
Interquartile range (IQR)199

Descriptive statistics

Standard deviation3576.1938
Coefficient of variation (CV)0.017728304
Kurtosis3177.4854
Mean201722.27
Median Absolute Deviation (MAD)96.5
Skewness-56.313782
Sum6.4349403 × 108
Variance12789162
MonotonicityNot monotonic
2025-06-18T10:02:00.311422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201611 246
 
7.7%
201701 150
 
4.7%
201703 140
 
4.4%
201706 136
 
4.3%
201704 133
 
4.2%
201705 128
 
4.0%
201612 126
 
3.9%
201702 116
 
3.6%
201708 109
 
3.4%
201707 107
 
3.4%
Other values (69) 1799
56.4%
ValueCountFrequency (%)
0 1
 
< 0.1%
201607 8
 
0.3%
201608 48
 
1.5%
201609 53
 
1.7%
201610 94
 
2.9%
201611 246
7.7%
201612 126
3.9%
201701 150
4.7%
201702 116
3.6%
201703 140
4.4%
ValueCountFrequency (%)
202212 1
 
< 0.1%
202211 4
 
0.1%
202210 7
0.2%
202209 9
0.3%
202208 8
0.3%
202207 6
 
0.2%
202206 9
0.3%
202205 16
0.5%
202204 14
0.4%
202203 13
0.4%

anio_preinscripcion
Categorical

High correlation 

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size215.0 KiB
2017
1279 
2016
575 
2018
520 
2019
275 
2020
245 
Other values (3)
296 

Length

Max length9
Median length4
Mean length4.0015674
Min length4

Characters and Unicode

Total characters12765
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2017 1279
40.1%
2016 575
18.0%
2018 520
16.3%
2019 275
 
8.6%
2020 245
 
7.7%
2021 191
 
6.0%
2022 104
 
3.3%
sin datos 1
 
< 0.1%

Length

2025-06-18T10:02:00.405889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:02:00.467533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2017 1279
40.1%
2016 575
18.0%
2018 520
16.3%
2019 275
 
8.6%
2020 245
 
7.7%
2021 191
 
6.0%
2022 104
 
3.3%
sin 1
 
< 0.1%
datos 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 3833
30.0%
0 3434
26.9%
1 2840
22.2%
7 1279
 
10.0%
6 575
 
4.5%
8 520
 
4.1%
9 275
 
2.2%
s 2
 
< 0.1%
i 1
 
< 0.1%
n 1
 
< 0.1%
Other values (5) 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12765
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 3833
30.0%
0 3434
26.9%
1 2840
22.2%
7 1279
 
10.0%
6 575
 
4.5%
8 520
 
4.1%
9 275
 
2.2%
s 2
 
< 0.1%
i 1
 
< 0.1%
n 1
 
< 0.1%
Other values (5) 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12765
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 3833
30.0%
0 3434
26.9%
1 2840
22.2%
7 1279
 
10.0%
6 575
 
4.5%
8 520
 
4.1%
9 275
 
2.2%
s 2
 
< 0.1%
i 1
 
< 0.1%
n 1
 
< 0.1%
Other values (5) 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12765
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 3833
30.0%
0 3434
26.9%
1 2840
22.2%
7 1279
 
10.0%
6 575
 
4.5%
8 520
 
4.1%
9 275
 
2.2%
s 2
 
< 0.1%
i 1
 
< 0.1%
n 1
 
< 0.1%
Other values (5) 5
 
< 0.1%

cant_procesos_adjudicado
Real number (ℝ)

High correlation 

Distinct133
Distinct (%)4.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean11.973032
Minimum1
Maximum1214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.8 KiB
2025-06-18T10:02:00.577029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q38
95-th percentile43
Maximum1214
Range1213
Interquartile range (IQR)7

Descriptive statistics

Standard deviation45.841038
Coefficient of variation (CV)3.8286908
Kurtosis258.67011
Mean11.973032
Median Absolute Deviation (MAD)2
Skewness13.583427
Sum38182
Variance2101.4008
MonotonicityNot monotonic
2025-06-18T10:02:00.675038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1038
32.5%
2 481
15.1%
3 299
 
9.4%
4 207
 
6.5%
5 131
 
4.1%
6 118
 
3.7%
7 92
 
2.9%
9 67
 
2.1%
8 60
 
1.9%
11 58
 
1.8%
Other values (123) 638
20.0%
ValueCountFrequency (%)
1 1038
32.5%
2 481
15.1%
3 299
 
9.4%
4 207
 
6.5%
5 131
 
4.1%
6 118
 
3.7%
7 92
 
2.9%
8 60
 
1.9%
9 67
 
2.1%
10 56
 
1.8%
ValueCountFrequency (%)
1214 1
< 0.1%
989 1
< 0.1%
613 1
< 0.1%
590 1
< 0.1%
569 1
< 0.1%
551 1
< 0.1%
468 1
< 0.1%
442 1
< 0.1%
417 1
< 0.1%
388 1
< 0.1%

monto_total_adjudicado
Real number (ℝ)

High correlation  Skewed 

Distinct3159
Distinct (%)99.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean50791020
Minimum0
Maximum1.0043163 × 1010
Zeros23
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size49.8 KiB
2025-06-18T10:02:00.768764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile59588.893
Q11046296.7
median6641275.6
Q337385833
95-th percentile1.6322073 × 108
Maximum1.0043163 × 1010
Range1.0043163 × 1010
Interquartile range (IQR)36339536

Descriptive statistics

Standard deviation2.4847837 × 108
Coefficient of variation (CV)4.8921712
Kurtosis849.82454
Mean50791020
Median Absolute Deviation (MAD)6426194.1
Skewness23.822106
Sum1.6197256 × 1011
Variance6.1741498 × 1016
MonotonicityNot monotonic
2025-06-18T10:02:00.880931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
 
0.7%
1419000 4
 
0.1%
364285.7143 2
 
0.1%
30600000 2
 
0.1%
58802999.64 2
 
0.1%
33686842.11 2
 
0.1%
254100 2
 
0.1%
860796.6469 1
 
< 0.1%
8117708.619 1
 
< 0.1%
3583916.625 1
 
< 0.1%
Other values (3149) 3149
98.7%
ValueCountFrequency (%)
0 23
0.7%
0.01 1
 
< 0.1%
0.023181818 1
 
< 0.1%
4.25 1
 
< 0.1%
30.6 1
 
< 0.1%
102 1
 
< 0.1%
193 1
 
< 0.1%
805.37 1
 
< 0.1%
1190 1
 
< 0.1%
1225 1
 
< 0.1%
ValueCountFrequency (%)
1.004316276 × 10101
< 0.1%
3114732192 1
< 0.1%
2731314502 1
< 0.1%
2721495852 1
< 0.1%
2576162114 1
< 0.1%
2548401882 1
< 0.1%
2255940286 1
< 0.1%
2252733473 1
< 0.1%
1823406361 1
< 0.1%
1724386756 1
< 0.1%

antiguedad
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.2442772
Minimum0
Maximum5
Zeros295
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size49.8 KiB
2025-06-18T10:02:00.958973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5130868
Coefficient of variation (CV)0.46638641
Kurtosis-0.27845748
Mean3.2442772
Median Absolute Deviation (MAD)1
Skewness-0.88035822
Sum10346
Variance2.2894316
MonotonicityNot monotonic
2025-06-18T10:02:01.006408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 1279
40.1%
5 575
18.0%
3 520
16.3%
0 295
 
9.2%
2 275
 
8.6%
1 245
 
7.7%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
0 295
 
9.2%
1 245
 
7.7%
2 275
 
8.6%
3 520
16.3%
4 1279
40.1%
5 575
18.0%
ValueCountFrequency (%)
5 575
18.0%
4 1279
40.1%
3 520
16.3%
2 275
 
8.6%
1 245
 
7.7%
0 295
 
9.2%

provincia
Categorical

High correlation 

Distinct27
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size364.4 KiB
Ciudad Autónoma de Buenos Aires
1610 
Buenos Aires
668 
Córdoba
192 
Santa Fe
 
143
Mendoza
 
61
Other values (22)
516 

Length

Max length31
Median length31
Mean length20.474295
Min length1

Characters and Unicode

Total characters65313
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowCiudad Autónoma de Buenos Aires
2nd rowCiudad Autónoma de Buenos Aires
3rd rowCiudad Autónoma de Buenos Aires
4th rowBuenos Aires
5th rowCiudad Autónoma de Buenos Aires

Common Values

ValueCountFrequency (%)
Ciudad Autónoma de Buenos Aires 1610
50.5%
Buenos Aires 668
20.9%
Córdoba 192
 
6.0%
Santa Fe 143
 
4.5%
Mendoza 61
 
1.9%
Chubut 48
 
1.5%
Rio Negro 39
 
1.2%
Tierra del Fuego 38
 
1.2%
Tucumán 36
 
1.1%
Neuquén 35
 
1.1%
Other values (17) 320
 
10.0%

Length

2025-06-18T10:02:01.080181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aires 2278
21.2%
buenos 2278
21.2%
autónoma 1610
15.0%
ciudad 1610
15.0%
de 1610
15.0%
córdoba 192
 
1.8%
santa 164
 
1.5%
fe 143
 
1.3%
mendoza 61
 
0.6%
del 58
 
0.5%
Other values (28) 725
 
6.8%

Most occurring characters

ValueCountFrequency (%)
7539
11.5%
e 6730
10.3%
u 5879
9.0%
d 5146
 
7.9%
s 4756
 
7.3%
o 4465
 
6.8%
n 4390
 
6.7%
a 4171
 
6.4%
i 4159
 
6.4%
A 3888
 
6.0%
Other values (30) 14190
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65313
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7539
11.5%
e 6730
10.3%
u 5879
9.0%
d 5146
 
7.9%
s 4756
 
7.3%
o 4465
 
6.8%
n 4390
 
6.7%
a 4171
 
6.4%
i 4159
 
6.4%
A 3888
 
6.0%
Other values (30) 14190
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65313
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7539
11.5%
e 6730
10.3%
u 5879
9.0%
d 5146
 
7.9%
s 4756
 
7.3%
o 4465
 
6.8%
n 4390
 
6.7%
a 4171
 
6.4%
i 4159
 
6.4%
A 3888
 
6.0%
Other values (30) 14190
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65313
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7539
11.5%
e 6730
10.3%
u 5879
9.0%
d 5146
 
7.9%
s 4756
 
7.3%
o 4465
 
6.8%
n 4390
 
6.7%
a 4171
 
6.4%
i 4159
 
6.4%
A 3888
 
6.0%
Other values (30) 14190
21.7%

cant_socios
Real number (ℝ)

High correlation  Missing 

Distinct18
Distinct (%)0.6%
Missing61
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean1.9977629
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.8 KiB
2025-06-18T10:02:01.142675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum31
Range30
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4147768
Coefficient of variation (CV)0.70818056
Kurtosis98.54815
Mean1.9977629
Median Absolute Deviation (MAD)1
Skewness7.074036
Sum6251
Variance2.0015935
MonotonicityNot monotonic
2025-06-18T10:02:01.220790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 1390
43.6%
1 1131
35.5%
3 399
 
12.5%
4 117
 
3.7%
5 41
 
1.3%
6 19
 
0.6%
8 9
 
0.3%
7 6
 
0.2%
12 3
 
0.1%
10 3
 
0.1%
Other values (8) 11
 
0.3%
(Missing) 61
 
1.9%
ValueCountFrequency (%)
1 1131
35.5%
2 1390
43.6%
3 399
 
12.5%
4 117
 
3.7%
5 41
 
1.3%
6 19
 
0.6%
7 6
 
0.2%
8 9
 
0.3%
9 2
 
0.1%
10 3
 
0.1%
ValueCountFrequency (%)
31 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
17 1
 
< 0.1%
15 1
 
< 0.1%
14 2
0.1%
12 3
0.1%
11 2
0.1%
10 3
0.1%
9 2
0.1%

cant_apercibimientos
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)3.3%
Missing3130
Missing (%)98.1%
Memory size199.6 KiB
1.0
59 
2.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters180
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.7%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 59
 
1.8%
2.0 1
 
< 0.1%
(Missing) 3130
98.1%

Length

2025-06-18T10:02:01.283284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:02:01.330800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 59
98.3%
2.0 1
 
1.7%

Most occurring characters

ValueCountFrequency (%)
. 60
33.3%
0 60
33.3%
1 59
32.8%
2 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 60
33.3%
0 60
33.3%
1 59
32.8%
2 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 60
33.3%
0 60
33.3%
1 59
32.8%
2 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 60
33.3%
0 60
33.3%
1 59
32.8%
2 1
 
0.6%

cant_suspensiones
Categorical

High correlation  Missing 

Distinct5
Distinct (%)11.4%
Missing3146
Missing (%)98.6%
Memory size199.5 KiB
1.0
20 
2.0
17 
4.0
3.0
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters132
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.3%

Sample

1st row2.0
2nd row4.0
3rd row3.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 20
 
0.6%
2.0 17
 
0.5%
4.0 3
 
0.1%
3.0 3
 
0.1%
5.0 1
 
< 0.1%
(Missing) 3146
98.6%

Length

2025-06-18T10:02:01.377676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:02:01.424548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 20
45.5%
2.0 17
38.6%
4.0 3
 
6.8%
3.0 3
 
6.8%
5.0 1
 
2.3%

Most occurring characters

ValueCountFrequency (%)
. 44
33.3%
0 44
33.3%
1 20
15.2%
2 17
 
12.9%
4 3
 
2.3%
3 3
 
2.3%
5 1
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 132
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 44
33.3%
0 44
33.3%
1 20
15.2%
2 17
 
12.9%
4 3
 
2.3%
3 3
 
2.3%
5 1
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 132
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 44
33.3%
0 44
33.3%
1 20
15.2%
2 17
 
12.9%
4 3
 
2.3%
3 3
 
2.3%
5 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 132
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 44
33.3%
0 44
33.3%
1 20
15.2%
2 17
 
12.9%
4 3
 
2.3%
3 3
 
2.3%
5 1
 
0.8%

cant_antecedentes
Real number (ℝ)

High correlation  Missing 

Distinct6
Distinct (%)6.6%
Missing3099
Missing (%)97.1%
Infinite0
Infinite (%)0.0%
Mean1.6813187
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.8 KiB
2025-06-18T10:02:01.487039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1535368
Coefficient of variation (CV)0.68609051
Kurtosis3.7479709
Mean1.6813187
Median Absolute Deviation (MAD)0
Skewness1.9868297
Sum153
Variance1.3306471
MonotonicityNot monotonic
2025-06-18T10:02:01.550977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 58
 
1.8%
2 18
 
0.6%
4 6
 
0.2%
3 6
 
0.2%
6 2
 
0.1%
5 1
 
< 0.1%
(Missing) 3099
97.1%
ValueCountFrequency (%)
1 58
1.8%
2 18
 
0.6%
3 6
 
0.2%
4 6
 
0.2%
5 1
 
< 0.1%
6 2
 
0.1%
ValueCountFrequency (%)
6 2
 
0.1%
5 1
 
< 0.1%
4 6
 
0.2%
3 6
 
0.2%
2 18
 
0.6%
1 58
1.8%

cant_Apoderado
Real number (ℝ)

High correlation  Missing 

Distinct7
Distinct (%)0.4%
Missing1496
Missing (%)46.9%
Infinite0
Infinite (%)0.0%
Mean1.2739079
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.8 KiB
2025-06-18T10:02:01.598406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum11
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6561566
Coefficient of variation (CV)0.51507381
Kurtosis37.426015
Mean1.2739079
Median Absolute Deviation (MAD)0
Skewness4.3922424
Sum2158
Variance0.43054148
MonotonicityNot monotonic
2025-06-18T10:02:01.660900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 1345
42.2%
2 273
 
8.6%
3 55
 
1.7%
4 12
 
0.4%
5 5
 
0.2%
6 3
 
0.1%
11 1
 
< 0.1%
(Missing) 1496
46.9%
ValueCountFrequency (%)
1 1345
42.2%
2 273
 
8.6%
3 55
 
1.7%
4 12
 
0.4%
5 5
 
0.2%
6 3
 
0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
6 3
 
0.1%
5 5
 
0.2%
4 12
 
0.4%
3 55
 
1.7%
2 273
 
8.6%
1 1345
42.2%

cant_representante
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)0.2%
Missing1055
Missing (%)33.1%
Memory size207.7 KiB
1.0
1889 
2.0
207 
3.0
 
33
4.0
 
5
10.0
 
1

Length

Max length4
Median length3
Mean length3.0004684
Min length3

Characters and Unicode

Total characters6406
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1889
59.2%
2.0 207
 
6.5%
3.0 33
 
1.0%
4.0 5
 
0.2%
10.0 1
 
< 0.1%
(Missing) 1055
33.1%

Length

2025-06-18T10:02:01.734236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:02:01.781179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1889
88.5%
2.0 207
 
9.7%
3.0 33
 
1.5%
4.0 5
 
0.2%
10.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 2136
33.3%
. 2135
33.3%
1 1890
29.5%
2 207
 
3.2%
3 33
 
0.5%
4 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6406
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2136
33.3%
. 2135
33.3%
1 1890
29.5%
2 207
 
3.2%
3 33
 
0.5%
4 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6406
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2136
33.3%
. 2135
33.3%
1 1890
29.5%
2 207
 
3.2%
3 33
 
0.5%
4 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6406
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2136
33.3%
. 2135
33.3%
1 1890
29.5%
2 207
 
3.2%
3 33
 
0.5%
4 5
 
0.1%

cant_autenticado
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.24365
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.8 KiB
2025-06-18T10:02:01.832428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum11
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.55491528
Coefficient of variation (CV)0.44619889
Kurtosis37.045149
Mean1.24365
Median Absolute Deviation (MAD)0
Skewness3.9479659
Sum3966
Variance0.30793096
MonotonicityNot monotonic
2025-06-18T10:02:01.879298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 2543
79.7%
2 545
 
17.1%
3 85
 
2.7%
4 9
 
0.3%
5 5
 
0.2%
6 1
 
< 0.1%
11 1
 
< 0.1%
(Missing) 1
 
< 0.1%
ValueCountFrequency (%)
1 2543
79.7%
2 545
 
17.1%
3 85
 
2.7%
4 9
 
0.3%
5 5
 
0.2%
6 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
6 1
 
< 0.1%
5 5
 
0.2%
4 9
 
0.3%
3 85
 
2.7%
2 545
 
17.1%
1 2543
79.7%

cant_noAutenticado
Real number (ℝ)

High correlation  Missing 

Distinct7
Distinct (%)1.5%
Missing2714
Missing (%)85.1%
Infinite0
Infinite (%)0.0%
Mean1.3109244
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.8 KiB
2025-06-18T10:02:01.941717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.83095784
Coefficient of variation (CV)0.63387169
Kurtosis60.937221
Mean1.3109244
Median Absolute Deviation (MAD)0
Skewness5.9756365
Sum624
Variance0.69049093
MonotonicityNot monotonic
2025-06-18T10:02:01.988586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 381
 
11.9%
2 61
 
1.9%
3 27
 
0.8%
4 3
 
0.1%
5 2
 
0.1%
6 1
 
< 0.1%
12 1
 
< 0.1%
(Missing) 2714
85.1%
ValueCountFrequency (%)
1 381
11.9%
2 61
 
1.9%
3 27
 
0.8%
4 3
 
0.1%
5 2
 
0.1%
6 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
6 1
 
< 0.1%
5 2
 
0.1%
4 3
 
0.1%
3 27
 
0.8%
2 61
 
1.9%
1 381
11.9%

cant_sinMontoLimite
Real number (ℝ)

High correlation  Missing 

Distinct9
Distinct (%)0.3%
Missing56
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean1.4304403
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.8 KiB
2025-06-18T10:02:02.049627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.77511899
Coefficient of variation (CV)0.54187439
Kurtosis31.56751
Mean1.4304403
Median Absolute Deviation (MAD)0
Skewness3.6666531
Sum4483
Variance0.60080945
MonotonicityNot monotonic
2025-06-18T10:02:02.114634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 2119
66.4%
2 786
 
24.6%
3 169
 
5.3%
4 37
 
1.2%
5 16
 
0.5%
6 3
 
0.1%
7 2
 
0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
(Missing) 56
 
1.8%
ValueCountFrequency (%)
1 2119
66.4%
2 786
 
24.6%
3 169
 
5.3%
4 37
 
1.2%
5 16
 
0.5%
6 3
 
0.1%
7 2
 
0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
12 1
 
< 0.1%
7 2
 
0.1%
6 3
 
0.1%
5 16
 
0.5%
4 37
 
1.2%
3 169
 
5.3%
2 786
 
24.6%
1 2119
66.4%

cant_MontoLimite
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)3.4%
Missing3101
Missing (%)97.2%
Memory size199.7 KiB
1.0
74 
2.0
14 
6.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters267
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row6.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 74
 
2.3%
2.0 14
 
0.4%
6.0 1
 
< 0.1%
(Missing) 3101
97.2%

Length

2025-06-18T10:02:02.177127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:02:02.223997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 74
83.1%
2.0 14
 
15.7%
6.0 1
 
1.1%

Most occurring characters

ValueCountFrequency (%)
. 89
33.3%
0 89
33.3%
1 74
27.7%
2 14
 
5.2%
6 1
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 267
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 89
33.3%
0 89
33.3%
1 74
27.7%
2 14
 
5.2%
6 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 267
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 89
33.3%
0 89
33.3%
1 74
27.7%
2 14
 
5.2%
6 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 267
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 89
33.3%
0 89
33.3%
1 74
27.7%
2 14
 
5.2%
6 1
 
0.4%

total_articulos_provee
Real number (ℝ)

High correlation 

Distinct426
Distinct (%)13.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean78.778614
Minimum1
Maximum6064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size49.8 KiB
2025-06-18T10:02:02.286488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median13
Q353
95-th percentile357.6
Maximum6064
Range6063
Interquartile range (IQR)50

Descriptive statistics

Standard deviation243.9568
Coefficient of variation (CV)3.096739
Kurtosis239.09958
Mean78.778614
Median Absolute Deviation (MAD)12
Skewness12.25787
Sum251225
Variance59514.922
MonotonicityNot monotonic
2025-06-18T10:02:02.382114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 488
 
15.3%
2 211
 
6.6%
3 145
 
4.5%
4 126
 
3.9%
6 109
 
3.4%
5 103
 
3.2%
7 81
 
2.5%
9 72
 
2.3%
8 71
 
2.2%
10 69
 
2.2%
Other values (416) 1714
53.7%
ValueCountFrequency (%)
1 488
15.3%
2 211
6.6%
3 145
 
4.5%
4 126
 
3.9%
5 103
 
3.2%
6 109
 
3.4%
7 81
 
2.5%
8 71
 
2.2%
9 72
 
2.3%
10 69
 
2.2%
ValueCountFrequency (%)
6064 1
< 0.1%
5765 1
< 0.1%
3605 1
< 0.1%
3387 1
< 0.1%
2330 1
< 0.1%
2189 1
< 0.1%
2007 1
< 0.1%
1935 1
< 0.1%
1715 1
< 0.1%
1622 1
< 0.1%

dmonto_total_adjudicado
Categorical

High correlation 

Distinct20
Distinct (%)0.6%
Missing1
Missing (%)< 0.1%
Memory size281.2 KiB
(46718747.516, 89439449.702]
411 
(30451916.51, 46718747.516]
 
199
(19975532.58, 30451916.51]
 
183
(9424898.401, 13557176.81]
 
178
(13557176.81, 19975532.58]
 
178
Other values (15)
2040 

Length

Max length29
Median length28
Mean length25.28222
Min length19

Characters and Unicode

Total characters80625
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(4727330.113, 6702697.888]
2nd row(6702697.888, 9424898.401]
3rd row(46718747.516, 89439449.702]
4th row(4727330.113, 6702697.888]
5th row(4727330.113, 6702697.888]

Common Values

ValueCountFrequency (%)
(46718747.516, 89439449.702] 411
 
12.9%
(30451916.51, 46718747.516] 199
 
6.2%
(19975532.58, 30451916.51] 183
 
5.7%
(9424898.401, 13557176.81] 178
 
5.6%
(13557176.81, 19975532.58] 178
 
5.6%
(89439449.702, 222964579.98] 167
 
5.2%
(3396600.0, 4727330.113] 164
 
5.1%
(6702697.888, 9424898.401] 157
 
4.9%
(4727330.113, 6702697.888] 155
 
4.9%
(1793326.755, 2483085.385] 151
 
4.7%
Other values (10) 1246
39.1%

Length

2025-06-18T10:02:02.479067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
46718747.516 610
 
9.6%
89439449.702 578
 
9.1%
30451916.51 382
 
6.0%
19975532.58 361
 
5.7%
13557176.81 356
 
5.6%
9424898.401 335
 
5.3%
4727330.113 319
 
5.0%
6702697.888 312
 
4.9%
3396600.0 305
 
4.8%
2483085.385 292
 
4.6%
Other values (11) 2528
39.6%

Most occurring characters

ValueCountFrequency (%)
7 7900
9.8%
1 7435
9.2%
9 7376
9.1%
. 6378
 
7.9%
8 6223
 
7.7%
5 6105
 
7.6%
3 6009
 
7.5%
4 5818
 
7.2%
0 5468
 
6.8%
6 4603
 
5.7%
Other values (6) 17310
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80625
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 7900
9.8%
1 7435
9.2%
9 7376
9.1%
. 6378
 
7.9%
8 6223
 
7.7%
5 6105
 
7.6%
3 6009
 
7.5%
4 5818
 
7.2%
0 5468
 
6.8%
6 4603
 
5.7%
Other values (6) 17310
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80625
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 7900
9.8%
1 7435
9.2%
9 7376
9.1%
. 6378
 
7.9%
8 6223
 
7.7%
5 6105
 
7.6%
3 6009
 
7.5%
4 5818
 
7.2%
0 5468
 
6.8%
6 4603
 
5.7%
Other values (6) 17310
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80625
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 7900
9.8%
1 7435
9.2%
9 7376
9.1%
. 6378
 
7.9%
8 6223
 
7.7%
5 6105
 
7.6%
3 6009
 
7.5%
4 5818
 
7.2%
0 5468
 
6.8%
6 4603
 
5.7%
Other values (6) 17310
21.5%

dcant_procesos_adjudicado
Categorical

High correlation 

Distinct10
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Memory size238.2 KiB
(0.999, 2.0]
1519 
(2.0, 3.0]
299 
(8.0, 12.0]
223 
(3.0, 4.0]
207 
(19.0, 39.0]
190 
Other values (5)
751 

Length

Max length14
Median length12
Mean length11.470367
Min length10

Characters and Unicode

Total characters36579
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(6.0, 8.0]
2nd row(19.0, 39.0]
3rd row(39.0, 1214.0]
4th row(0.999, 2.0]
5th row(6.0, 8.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 1519
47.6%
(2.0, 3.0] 299
 
9.4%
(8.0, 12.0] 223
 
7.0%
(3.0, 4.0] 207
 
6.5%
(19.0, 39.0] 190
 
6.0%
(12.0, 19.0] 176
 
5.5%
(39.0, 1214.0] 174
 
5.5%
(6.0, 8.0] 152
 
4.8%
(4.0, 5.0] 131
 
4.1%
(5.0, 6.0] 118
 
3.7%
(Missing) 1
 
< 0.1%

Length

2025-06-18T10:02:02.547099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:02:02.616491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 1818
28.5%
0.999 1519
23.8%
3.0 506
 
7.9%
12.0 399
 
6.3%
8.0 375
 
5.9%
19.0 366
 
5.7%
39.0 364
 
5.7%
4.0 338
 
5.3%
6.0 270
 
4.2%
5.0 249
 
3.9%

Most occurring characters

ValueCountFrequency (%)
0 6378
17.4%
. 6378
17.4%
9 5287
14.5%
( 3189
8.7%
, 3189
8.7%
3189
8.7%
] 3189
8.7%
2 2391
 
6.5%
1 1113
 
3.0%
3 870
 
2.4%
Other values (4) 1406
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36579
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6378
17.4%
. 6378
17.4%
9 5287
14.5%
( 3189
8.7%
, 3189
8.7%
3189
8.7%
] 3189
8.7%
2 2391
 
6.5%
1 1113
 
3.0%
3 870
 
2.4%
Other values (4) 1406
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36579
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6378
17.4%
. 6378
17.4%
9 5287
14.5%
( 3189
8.7%
, 3189
8.7%
3189
8.7%
] 3189
8.7%
2 2391
 
6.5%
1 1113
 
3.0%
3 870
 
2.4%
Other values (4) 1406
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36579
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6378
17.4%
. 6378
17.4%
9 5287
14.5%
( 3189
8.7%
, 3189
8.7%
3189
8.7%
] 3189
8.7%
2 2391
 
6.5%
1 1113
 
3.0%
3 870
 
2.4%
Other values (4) 1406
 
3.8%

dtotal_articulos_provee
Categorical

High correlation 

Distinct15
Distinct (%)0.5%
Missing1
Missing (%)< 0.1%
Memory size239.5 KiB
(0.999, 2.0]
699 
(4.0, 6.0]
212 
(58.0, 97.6]
202 
(161.0, 345.0]
198 
(11.0, 15.0]
195 
Other values (10)
1683 

Length

Max length15
Median length12
Mean length11.885858
Min length10

Characters and Unicode

Total characters37904
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(161.0, 345.0]
2nd row(97.6, 161.0]
3rd row(58.0, 97.6]
4th row(29.0, 40.0]
5th row(21.0, 29.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 699
21.9%
(4.0, 6.0] 212
 
6.6%
(58.0, 97.6] 202
 
6.3%
(161.0, 345.0] 198
 
6.2%
(11.0, 15.0] 195
 
6.1%
(97.6, 161.0] 195
 
6.1%
(8.0, 11.0] 192
 
6.0%
(15.0, 21.0] 183
 
5.7%
(40.0, 58.0] 175
 
5.5%
(21.0, 29.0] 174
 
5.5%
Other values (5) 764
23.9%

Length

2025-06-18T10:02:02.725853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2.0 844
13.2%
0.999 699
 
11.0%
97.6 397
 
6.2%
161.0 393
 
6.2%
11.0 387
 
6.1%
15.0 378
 
5.9%
58.0 377
 
5.9%
345.0 367
 
5.8%
6.0 364
 
5.7%
21.0 357
 
5.6%
Other values (6) 1815
28.5%

Most occurring characters

ValueCountFrequency (%)
. 6378
16.8%
0 6328
16.7%
( 3189
8.4%
, 3189
8.4%
3189
8.4%
] 3189
8.4%
9 3178
8.4%
1 2295
 
6.1%
2 1547
 
4.1%
6 1323
 
3.5%
Other values (5) 4099
10.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 6378
16.8%
0 6328
16.7%
( 3189
8.4%
, 3189
8.4%
3189
8.4%
] 3189
8.4%
9 3178
8.4%
1 2295
 
6.1%
2 1547
 
4.1%
6 1323
 
3.5%
Other values (5) 4099
10.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 6378
16.8%
0 6328
16.7%
( 3189
8.4%
, 3189
8.4%
3189
8.4%
] 3189
8.4%
9 3178
8.4%
1 2295
 
6.1%
2 1547
 
4.1%
6 1323
 
3.5%
Other values (5) 4099
10.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 6378
16.8%
0 6328
16.7%
( 3189
8.4%
, 3189
8.4%
3189
8.4%
] 3189
8.4%
9 3178
8.4%
1 2295
 
6.1%
2 1547
 
4.1%
6 1323
 
3.5%
Other values (5) 4099
10.8%

cluster_k_4
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size205.6 KiB
0
3190 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3190
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3190
100.0%

Length

2025-06-18T10:02:02.788346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-18T10:02:02.833680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3190
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3190
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3190
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3190
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3190
100.0%

Interactions

2025-06-18T10:01:54.404316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:00:49.722049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:22.347516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:26.483447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:30.398502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:34.251554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:38.049326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:41.733136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:42.662017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:45.018220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:48.598864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:50.601686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:57.347569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:00:54.950545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:25.382303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:29.499088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:33.439375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:37.267968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:40.968194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:42.012042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:44.285329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:47.863204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:49.883960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:53.633833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:57.434738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:00:57.882399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:25.466423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:29.599880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:33.534441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:37.350354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:41.047752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:42.085477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:44.348788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:47.933273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:49.959255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:53.715747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:57.499187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:00.754310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:25.548348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:29.681765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:33.617608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:37.415319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:41.118296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:42.141431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:44.416552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:48.015660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:50.029459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:53.784836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:57.582510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:03.791227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:25.642081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:29.765798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:33.682727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:37.483032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:41.182063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:42.197373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:44.498846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:48.082312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:50.098042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:53.849049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:57.650179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:06.549570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:25.715300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:29.848547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:33.766071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:37.549688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:41.249800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:42.250160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:44.565088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:48.149805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:50.162309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:53.916720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:57.732477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:09.500771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:25.794035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:29.916078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:33.832934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:37.631829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:41.333224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:42.323694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:44.630790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:48.216624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:50.229047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:53.983290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:57.783386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:10.017409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:25.863813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:29.999336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:33.906664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:37.700647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:41.397347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:42.366551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:44.683249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:48.275703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:50.287618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:54.052397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:57.850164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:11.534094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:25.931164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:30.101296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:33.966330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:37.765338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:41.470124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:42.429082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:44.733178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:48.334514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:50.348827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:54.117509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:57.932507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:14.522115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:26.009272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:30.167429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:34.032839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:37.833549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:41.532062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:42.483362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:44.799898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:48.399899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:50.410536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:54.184778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:58.000021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:16.068043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:26.081689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:30.248881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:34.110794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:37.901911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:41.600294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:42.545409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:44.866492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:48.463257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:50.471883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:54.249975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:58.068516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:19.103286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:26.390706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:30.315150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:34.166276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:37.965154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:41.666438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:42.599852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:44.933176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:48.516437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:50.533241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-18T10:01:54.318195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-18T10:02:02.896247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CUITEstadoTipoSocietarioanio_preinscripcionantiguedadcant_Apoderadocant_MontoLimitecant_antecedentescant_apercibimientoscant_autenticadocant_noAutenticadocant_procesos_adjudicadocant_representantecant_sinMontoLimitecant_socioscant_suspensionesdcant_procesos_adjudicadodmonto_total_adjudicadodtotal_articulos_proveemonto_total_adjudicadoperiodo_preinscripcionprovinciatotal_articulos_provee
CUIT1.0001.0001.0001.000-0.321-0.0671.0000.1571.000-0.055-0.129-0.1101.000-0.105-0.0491.0001.0001.0001.000-0.1130.3141.0000.098
Estado1.0001.0000.0840.1060.1100.0000.1140.2700.0000.0000.0270.0000.0620.0150.0000.2390.0940.0940.0780.0831.0000.0000.063
TipoSocietario1.0000.0841.0000.1300.1400.0810.0350.0000.0000.0860.0380.0390.0730.0650.0800.0000.0750.2510.0550.0001.0000.0750.000
anio_preinscripcion1.0000.1060.1301.0001.0000.0000.0000.1270.2080.0000.1050.0070.0570.0260.0350.0000.1330.0990.0470.0000.9990.1900.040
antiguedad-0.3210.1100.1401.0001.0000.0350.000-0.0690.208-0.0310.1230.3420.0380.0320.0460.0000.1600.1150.0520.200-0.9620.1070.062
cant_Apoderado-0.0670.0000.0810.0000.0351.0000.7000.0571.0000.4390.5020.0650.0800.5840.0790.0000.0440.0000.0220.097-0.0440.000-0.023
cant_MontoLimite1.0000.1140.0350.0000.0000.7001.0001.000NaN0.6991.0000.0000.1210.0000.000NaN0.0000.3930.0001.0001.0000.0000.000
cant_antecedentes0.1570.2700.0000.127-0.0690.0571.0001.0000.2630.146-0.2050.0900.283-0.059-0.2810.6560.1540.2470.000-0.0370.0790.0000.367
cant_apercibimientos1.0000.0000.0000.2080.2081.000NaN0.2631.0000.0001.0000.3220.0000.0001.0001.0000.0000.0000.0000.0001.0000.0000.643
cant_autenticado-0.0550.0000.0860.000-0.0310.4390.6990.1460.0001.000-0.0010.0250.1620.7090.0590.0000.0210.0000.0180.0400.0390.000-0.043
cant_noAutenticado-0.1290.0270.0380.1050.1230.5021.000-0.2051.000-0.0011.0000.0030.5640.7180.1951.0000.0000.0270.0000.076-0.1280.2230.053
cant_procesos_adjudicado-0.1100.0000.0390.0070.3420.0650.0000.0900.3220.0250.0031.0000.0000.064-0.0070.0000.1740.0710.0750.595-0.3590.0000.312
cant_representante1.0000.0620.0730.0570.0380.0800.1210.2830.0000.1620.5640.0001.0000.4840.0240.7700.0310.0270.0000.0001.0000.0740.022
cant_sinMontoLimite-0.1050.0150.0650.0260.0320.5840.000-0.0590.0000.7090.7180.0640.4841.0000.1090.0000.0280.0460.0120.101-0.0290.000-0.043
cant_socios-0.0490.0000.0800.0350.0460.0790.000-0.2811.0000.0590.195-0.0070.0240.1091.0000.0000.0190.0200.0000.054-0.0440.000-0.038
cant_suspensiones1.0000.2390.0000.0000.0000.000NaN0.6561.0000.0001.0000.0000.7700.0000.0001.0000.1930.4710.1620.2891.0000.3140.328
dcant_procesos_adjudicado1.0000.0940.0750.1330.1600.0440.0000.1540.0000.0210.0000.1740.0310.0280.0190.1931.0000.2140.1200.0670.0000.0320.090
dmonto_total_adjudicado1.0000.0940.2510.0990.1150.0000.3930.2470.0000.0000.0270.0710.0270.0460.0200.4710.2141.0000.0360.1930.0000.0580.049
dtotal_articulos_provee1.0000.0780.0550.0470.0520.0220.0000.0000.0000.0180.0000.0750.0000.0120.0000.1620.1200.0361.0000.0001.0000.0320.316
monto_total_adjudicado-0.1130.0830.0000.0000.2000.0971.000-0.0370.0000.0400.0760.5950.0000.1010.0540.2890.0670.1930.0001.000-0.2240.0000.099
periodo_preinscripcion0.3141.0001.0000.999-0.962-0.0441.0000.0791.0000.039-0.128-0.3591.000-0.029-0.0441.0000.0000.0001.000-0.2241.0000.438-0.078
provincia1.0000.0000.0750.1900.1070.0000.0000.0000.0000.0000.2230.0000.0740.0000.0000.3140.0320.0580.0320.0000.4381.0000.061
total_articulos_provee0.0980.0630.0000.0400.062-0.0230.0000.3670.643-0.0430.0530.3120.022-0.043-0.0380.3280.0900.0490.3160.099-0.0780.0611.000

Missing values

2025-06-18T10:01:58.200112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-18T10:01:58.381785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-18T10:01:58.629397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveecluster_k_4
130569211685Electricidad Chiclana de R. Santoianni y O.S. Rodriguez18/08/2016Desactualizado Por Documentos VencidosSociedades De Hecho20160820167.04.975162e+065.0Ciudad Autónoma de Buenos Aires1.0NaNNaNNaN1.0NaN1.0NaN1.0NaN330.0(4727330.113, 6702697.888](6.0, 8.0](161.0, 345.0]0
230711500363LICICOM S.R.L.15/09/2016InscriptoSociedad Responsabilidad Limitada201609201622.07.425067e+065.0Ciudad Autónoma de Buenos Aires2.01.02.04.02.01.02.01.03.0NaN105.0(6702697.888, 9424898.401](19.0, 39.0](97.6, 161.0]0
330708415487YLUM S.A.24/08/2016InscriptoSociedad Anónima2016082016111.05.959313e+075.0Ciudad Autónoma de Buenos Aires1.0NaNNaNNaN1.0NaN1.0NaN1.0NaN83.0(46718747.516, 89439449.702](39.0, 1214.0](58.0, 97.6]0
433712286089COMPAÑÍA DE HIGIENE26/10/2016InscriptoSociedad Responsabilidad Limitada20161020161.05.434774e+065.0Buenos Aires2.0NaNNaNNaNNaN1.01.0NaN1.0NaN38.0(4727330.113, 6702697.888](0.999, 2.0](29.0, 40.0]0
530583184305MATAFUEGOS ORLANDO S.R.L.02/11/2016Desactualizado Por Documentos VencidosSociedad Responsabilidad Limitada20161120167.05.655964e+065.0Ciudad Autónoma de Buenos Aires3.0NaNNaNNaNNaN1.01.0NaN1.0NaN22.0(4727330.113, 6702697.888](6.0, 8.0](21.0, 29.0]0
630708538317CAROLS SA09/09/2016InscriptoSociedad Anónima201609201633.07.614297e+075.0Ciudad Autónoma de Buenos Aires2.0NaNNaNNaN1.0NaN1.0NaN1.0NaN92.0(46718747.516, 89439449.702](19.0, 39.0](58.0, 97.6]0
730521417311CONFECCIONES JOSE CONTARTESE Y CIA S.R.L.12/09/2016InscriptoSociedad Responsabilidad Limitada201609201637.01.823406e+095.0Ciudad Autónoma de Buenos Aires2.0NaNNaNNaN1.0NaN1.0NaN1.0NaN50.0(222964579.98, 46172150151.0](19.0, 39.0](40.0, 58.0]0
1230708516852COOPERATIVA DE TRABAJO ARCANGEL LIMITADA19/10/2016InscriptoCooperativas20161020166.02.781002e+075.0Buenos Aires4.0NaNNaNNaN1.0NaN1.0NaN1.0NaN2.0(19975532.58, 30451916.51](5.0, 6.0](0.999, 2.0]0
1430710210000TECNARAN SRL06/09/2016InscriptoSociedad Responsabilidad Limitada201609201619.01.289448e+085.0Ciudad Autónoma de Buenos Aires1.0NaNNaNNaN1.01.02.0NaN2.0NaN1.0(89439449.702, 222964579.98](12.0, 19.0](0.999, 2.0]0
1830714005789SIMA POWER SYSTEMS SRL16/08/2016Desactualizado Por ClaseSociedad Responsabilidad Limitada20160820161.09.405250e+055.0Ciudad Autónoma de Buenos Aires1.0NaNNaNNaNNaN1.01.0NaN1.0NaN8.0(890758.9, 1302657.558](0.999, 2.0](6.0, 8.0]0
CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveecluster_k_4
1003230717686043LOZZA SAS22/08/2022InscriptoOtras Formas Societarias20220820221.05.747619e+060.0Entre Rios1.0NaNNaNNaNNaN1.01.0NaN1.0NaN40.0(4727330.113, 6702697.888](0.999, 2.0](29.0, 40.0]0
1003530711231214Encendido Bari SRL19/04/2018InscriptoSociedad Responsabilidad Limitada20180420181.01.179683e+063.0Ciudad Autónoma de Buenos Aires2.0NaNNaNNaNNaN1.01.0NaN1.0NaN69.0(890758.9, 1302657.558](0.999, 2.0](58.0, 97.6]0
1003830527725247SODA DI MARCO S.R.L.16/10/2018InscriptoSociedad Responsabilidad Limitada20181020181.06.557143e+043.0Mendoza3.0NaNNaNNaN2.0NaN2.0NaN2.0NaN3.0(33011.111, 104767.373](0.999, 2.0](2.0, 3.0]0
1004230717549968GRUPO LUGINO SRL02/08/2022Desactualizado Por Mantencion FormularioSociedad Responsabilidad Limitada20220820221.01.930000e+020.0Ciudad Autónoma de Buenos Aires2.0NaNNaNNaN1.0NaN1.0NaN1.0NaN1935.0(-0.001, 33011.111](0.999, 2.0](345.0, 6993.0]0
1004330713790407PEDRO GOITIA S.R.L.20/03/2017InscriptoSociedad Responsabilidad Limitada20170320171.01.997880e+074.0Ciudad Autónoma de Buenos Aires3.0NaNNaNNaN1.01.02.0NaN2.0NaN9.0(19975532.58, 30451916.51](0.999, 2.0](8.0, 11.0]0
1004730518800910Consejo Latinoamericano de Ciencias Sociales20/09/2021Pre InscriptoOtras Formas Societarias20210920211.06.560265e+060.0Ciudad Autónoma de Buenos Aires1.0NaNNaNNaN1.0NaN1.0NaN1.0NaN7.0(4727330.113, 6702697.888](0.999, 2.0](6.0, 8.0]0
1004830714288551AGUAS ROMANO SRL01/12/2016InscriptoSociedad Responsabilidad Limitada20161220161.04.215306e+055.0Entre Rios2.0NaNNaNNaNNaN1.01.0NaN1.0NaN1.0(377939.298, 599760.0](0.999, 2.0](0.999, 2.0]0
1005330715900560VINXO CRUZ SRL30/08/2022Pre InscriptoSociedad Responsabilidad Limitada20220820221.03.471109e+050.0Mendoza1.0NaNNaNNaN1.0NaN1.0NaN1.0NaN241.0(224078.198, 377939.298](0.999, 2.0](161.0, 345.0]0
1007030710308051ZENSEI SRL30/05/2017InscriptoSociedad Anónima20170520171.05.901429e+074.0Ciudad Autónoma de Buenos Aires1.0NaNNaNNaNNaN1.01.0NaN1.0NaN1.0(46718747.516, 89439449.702](0.999, 2.0](0.999, 2.0]0
1007430716441098LISTOS PARA RODAR SAS16/08/2019InscriptoOtras Formas Societarias20190820191.01.196939e+052.0Ciudad Autónoma de Buenos Aires2.0NaNNaNNaN1.0NaN1.0NaN1.0NaN19.0(104767.373, 224078.198](0.999, 2.0](15.0, 21.0]0